| # | Player | W | F | S | Q | R16 | R32 | R64 | R128 | APPS | PTS | PTS/APP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151. | Donna Vekić | 0 | 0 | 1 | 2 | 5 | 6 | 15 | 22 | 51 | 4890 | 96 |
| 152. | Kathy Rinaldi | 0 | 0 | 1 | 2 | 5 | 5 | 13 | 19 | 45 | 4590 | 102 |
| 153. | Paula Badosa | 0 | 0 | 1 | 2 | 5 | 4 | 5 | 7 | 24 | 3780 | 158 |
| 154. | Anna Chakvetadze | 0 | 0 | 1 | 2 | 2 | 6 | 8 | 9 | 28 | 3550 | 127 |
| 155. | Emma Navarro | 0 | 0 | 1 | 2 | 2 | 3 | 2 | 5 | 15 | 2700 | 180 |
| 156. | Maria Bueno | 0 | 0 | 1 | 2 | 1 | 2 | 2 | 0 | 8 | 2280 | 285 |
| 157. | Chris Matison | 0 | 0 | 1 | 2 | 0 | 4 | 5 | 2 | 14 | 2530 | 181 |
| 158. | Mary Sawyer | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 0 | 4 | 1770 | 443 |
| 159. | Irina Spîrlea | 0 | 0 | 1 | 1 | 7 | 5 | 3 | 11 | 28 | 3860 | 138 |
| 160. | Anna Kournikova | 0 | 0 | 1 | 1 | 7 | 3 | 4 | 5 | 21 | 3610 | 172 |
| 161. | Bettina Bunge | 0 | 0 | 1 | 1 | 6 | 11 | 8 | 3 | 30 | 4670 | 156 |
| 162. | Caroline Garcia | 0 | 0 | 1 | 1 | 6 | 9 | 17 | 19 | 53 | 5200 | 98 |
| 163. | Kerry Harris | 0 | 0 | 1 | 1 | 5 | 5 | 8 | 2 | 22 | 3640 | 165 |
| 164. | Barbora Strýcová | 0 | 0 | 1 | 1 | 4 | 10 | 14 | 29 | 59 | 4740 | 80 |
| 165. | Kiki Bertens | 0 | 0 | 1 | 1 | 3 | 6 | 7 | 16 | 34 | 3360 | 99 |
| 166. | Beatriz Haddad Maia | 0 | 0 | 1 | 1 | 2 | 3 | 9 | 9 | 25 | 2800 | 112 |
| 167. | Raquel Giscafré | 0 | 0 | 1 | 1 | 2 | 2 | 12 | 4 | 22 | 2830 | 129 |
| 168. | Natasha Chmyreva | 0 | 0 | 1 | 1 | 2 | 1 | 1 | 0 | 6 | 1890 | 315 |
| 169. | Claudia Porwik | 0 | 0 | 1 | 1 | 1 | 3 | 8 | 18 | 32 | 2580 | 81 |
| 170. | Martina Trevisan | 0 | 0 | 1 | 1 | 0 | 0 | 5 | 11 | 18 | 1670 | 93 |
| 171. | Julia Görges | 0 | 0 | 1 | 0 | 6 | 9 | 13 | 20 | 49 | 4500 | 92 |
| 172. | Peng Shuai | 0 | 0 | 1 | 0 | 6 | 6 | 18 | 20 | 51 | 4460 | 87 |
| 173. | Nathalie Dechy | 0 | 0 | 1 | 0 | 5 | 15 | 12 | 22 | 55 | 4990 | 91 |
| 174. | Yanina Wickmayer | 0 | 0 | 1 | 0 | 4 | 7 | 13 | 25 | 50 | 3810 | 76 |
| 175. | Nicole Bradtke | 0 | 0 | 1 | 0 | 4 | 7 | 13 | 16 | 41 | 3720 | 91 |
| 176. | Elena Vesnina | 0 | 0 | 1 | 0 | 3 | 10 | 17 | 20 | 51 | 4190 | 82 |
| 177. | Yvonne Vermaak | 0 | 0 | 1 | 0 | 2 | 6 | 14 | 10 | 33 | 3120 | 95 |
| 178. | Dayana Yastremska | 0 | 0 | 1 | 0 | 1 | 7 | 3 | 14 | 26 | 2280 | 88 |
| 179. | Magdaléna Rybáriková | 0 | 0 | 1 | 0 | 1 | 5 | 12 | 25 | 44 | 2760 | 63 |
| 180. | Fabiola Zuluaga | 0 | 0 | 1 | 0 | 1 | 5 | 8 | 4 | 19 | 2270 | 119 |
| 181. | Brigitte Simon | 0 | 0 | 1 | 0 | 1 | 4 | 3 | 4 | 13 | 1790 | 138 |
| 182. | Kirsten Flipkens | 0 | 0 | 1 | 0 | 1 | 3 | 24 | 21 | 50 | 3300 | 66 |
| 183. | Marijke Schaar | 0 | 0 | 1 | 0 | 1 | 3 | 6 | 2 | 13 | 1850 | 142 |
| 184. | Diana Shnaider | 0 | 0 | 1 | 0 | 1 | 3 | 5 | 3 | 13 | 1790 | 138 |
| 185. | Belinda Cordwell | 0 | 0 | 1 | 0 | 1 | 3 | 3 | 5 | 13 | 1670 | 128 |
| 186. | Meredith McGrath | 0 | 0 | 1 | 0 | 1 | 1 | 5 | 12 | 20 | 1620 | 81 |
| 187. | Camille Benjamin | 0 | 0 | 1 | 0 | 0 | 11 | 11 | 12 | 35 | 3100 | 89 |
| 188. | Magda Linette | 0 | 0 | 1 | 0 | 0 | 9 | 7 | 27 | 44 | 2710 | 62 |
| 189. | Marianne Werdel | 0 | 0 | 1 | 0 | 0 | 4 | 9 | 23 | 37 | 2160 | 58 |
| 190. | Diane Evers | 0 | 0 | 1 | 0 | 0 | 3 | 6 | 2 | 12 | 1610 | 134 |
| 191. | Tamara Zidanšek | 0 | 0 | 1 | 0 | 0 | 2 | 5 | 12 | 20 | 1510 | 76 |
| 192. | Elisabeth Ekblom | 0 | 0 | 1 | 0 | 0 | 2 | 2 | 8 | 13 | 1260 | 97 |
| 193. | Tatjana Maria | 0 | 0 | 1 | 0 | 0 | 1 | 14 | 35 | 51 | 2240 | 44 |
| 194. | Clarisa Fernández | 0 | 0 | 1 | 0 | 0 | 1 | 4 | 10 | 16 | 1290 | 81 |
| 195. | Nadia Podoroska | 0 | 0 | 1 | 0 | 0 | 0 | 6 | 10 | 17 | 1300 | 76 |
| 196. | Alexandra Stevenson | 0 | 0 | 1 | 0 | 0 | 0 | 4 | 16 | 21 | 1220 | 58 |
| 197. | Annette Van Zyl | 0 | 0 | 1 | 0 | 0 | 0 | 4 | 0 | 5 | 1060 | 212 |
| 198. | Loïs Boisson | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 4 | 810 | 203 |
| 199. | Carla Suárez Navarro | 0 | 0 | 0 | 7 | 9 | 10 | 11 | 11 | 48 | 7350 | 153 |
| 200. | Kaia Kanepi | 0 | 0 | 0 | 7 | 3 | 7 | 18 | 24 | 59 | 6140 | 104 |
Open Era Grand Slam Tennis
This site is dedicated to Grand Slam tennis stats from the Open Era – for those of us who love the numbers behind the game. Pick any group of players and see how they rank. Compare, filter, and view the same data from different angles – whether you want to explore dominance, consistency, or longevity. In short: It’s all about turning tennis history into insights you can actually play with.